An overview of soil heterogeneity: quantification and implications on geotechnical field problems
Bibliographic record
Abstract
Engineering judgment and reliance on factors of safety have been the conventional tools for dealing with soil heterogeneity in geotechnical practice. This paper presents a review of recent advances in treating soil variability. It presents the implications of geostatistical techniques and up-scaling methods used for quantifying the heterogeneous permeability of soil as addressed in the petroleum industry. Moreover, the interest of geotechnical practice to incorporate the statistical properties of soil in a probabilistic design framework is also discussed. This ranges from conventional Monte Carlo simulation based design and stochastic finite element analysis to the recent techniques that take into account the effect of spatial correlation of soil properties. Example applications of these techniques to different types of field problems, such as foundation settlement, seepage flow, and liquefaction assessment, are discussed with emphasis on the limitations of the current practice and trends for future research. In addition, different decision making algorithms are addressed with examples of their applications to geotechnical field problems.Key words: heterogeneity, spatial variability, geostatistics, stochastic analysis, decision making.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".